regex-vs-llm-structured-text

A practical decision framework for parsing structured text (quizzes, forms, invoices, documents). The key insight: regex handles 95-98% of cases cheaply and deterministically. Reserve expensive LLM calls for the remaining edge cases. Flag items that may need LLM review: From a production quiz parsing pipeline (410 items): - Parsing structured text with repeating patterns (questions, forms, tables) - Deciding between regex and LLM for text extraction - Building hybrid pipelines that combine both approaches - Optimizing cost/accuracy tradeoffs in text processing - Start with regex — even imperfect regex gives you a baseline to improve - Use confidence scoring to programmatically identify what needs LLM help - Use the cheapest LLM for validation (Haiku-class models are sufficient) - Never mutate parsed items — return new instances from cleaning/validation steps - TDD works well for parsers — write tests for known patterns first, then edge cases - Log metrics (regex success rate, LLM call count) to track pipeline health - Sending all text to an LLM when regex handles 95%+ of cases (expensive and slow) - Using regex for free-form, highly variable text (LLM is better here) - Skipping confidence scoring and hoping regex "just works" - Mutating parsed objects during cleaning/validation steps - Not testing edge cases (malformed input, missing fields, encoding issues) - Quiz/exam...